How to change values of rows based on conditions in dataframe?

Question

I have dataframe that is shown below,

    type  label
0      0      0
1      0      0
2      0      0
3      0      0
4      2      1
5      2      1
6      2      1
7      2      1
8      2      1
9      2      1
10     0      0
11     0      0
12     0      0
13     0      0
14     0      0
15     0      0
16     0      0
17     0      0
18     0      0
19     0      0

(Need some magic to be done)

The change should be made in type column such that, in a row if label is 0 and type is 0 then next row of type should be assigned 2.

Full dataframe should look like this:

    type  label
0      0      0
1      2      0
2      2      0
3      0      0
4      2      1
5      2      1
6      2      1
7      2      1
8      2      1
9      2      1
10     0      0
11     2      0
12     2      0
13     2      0
14     2      0
15     2      0
16     2      0
17     2      0
18     2      0
19     2      0

bharatk · Accepted Answer · 2019-09-23 10:25:17Z

1

Using .eq() to mask row which has type and label columns value is 0 and .shift() to shift index by desired number of periods with an optional time freq.

Ex.

m = df['type'].eq(0) & df['label'].eq(0)
df.loc[m == m.shift(1),'type'] = 2
print(df)

O/P:

    type  label
0      0      0
1      2      0
2      2      0
3      2      0
4      2      1
5      2      1
6      2      1
7      2      1
8      2      1
9      2      1
10     0      0
11     2      0
12     2      0
13     2      0
14     2      0
15     2      0
16     2      0
17     2      0
18     2      0
19     2      0

answered Sep 23, 2019 at 10:25

bharatk

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Comments

Antonios · Accepted Answer · 2019-09-23 15:18:05Z

Similar approach

df['Condition']=(df==0).all(axis=1)
df['Condition']=df['Condition'].shift(periods=1).fillna(False)
df['Type'] = np.where(df['Condition'], 2, df['Type'])

Type  label  Condition
0      0      0      False
1      2      0       True
2      2      0       True
3      2      0       True
4      2      1       True
5      2      1      False
6      2      1      False
7      2      1      False
8      2      1      False
9      2      1      False
10     0      0      False
11     2      0       True
12     2      0       True
13     2      0       True
14     2      0       True
15     2      0       True
16     2      0       True
17     2      0       True
18     2      0       True
19     2      0       True

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